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Record W2291465512 · doi:10.1139/cgj-2015-0222

Prediction of the variation of swelling pressure and one-dimensional heave of expansive soils with respect to suction using the soil-water retention curve as a tool

2016· article· en· W2291465512 on OpenAlexaffvenueabout
Hongyu Tu, Sai K. Vanapalli

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOedometer testExpansive clayGeotechnical engineeringSuctionSwellingSoil waterOverburden pressureWater contentGeologySoil scienceEngineering

Abstract

fetched live from OpenAlex

The one-dimensional (1-D) potential heave (or swell strain) of expansive soil is conventionally estimated using the swelling pressure and swelling index values that are determined from different types of oedometer test results. The swelling pressure of expansive soils is typically measured at saturated condition from oedometer tests. The experimental procedures of oedometer tests are cumbersome as well as time-consuming for use in conventional geotechnical engineering practice and are not capable of estimating heave under different stages of unsaturated conditions. To alleviate these limitations, semi-empirical models are proposed to predict the variation of swelling pressure of both compacted and natural expansive soils with respect to soil suction using the soil-water retention curve (SWRC) as a tool. An empirical relationship is also suggested for estimating the swelling index from plasticity index values, alleviating the need for conducting oedometer tests. The predicted swelling pressure and estimated swelling index are then used to estimate the variation of 1-D heave with respect to suction for expansive soils by modifying Fredlund’s 1983 equation. The proposed approach is validated for eight field sites from six countries — namely, Saudi Arabia, Australia, Canada, China, USA, and UK — and on six different compacted expansive soils from USA.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.194
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations62
Published2016
Admission routes3
Has abstractyes

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